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A new model to predict intravenous immunoglobin-resistant Kawasaki disease
OBJECTIVES: To clarify the independent risk factors and construct predictive model for intravenous immunoglobin (IVIG)-resistant KD (IVIGRKD). RESULTS: The ratio of male to female in the overall samples was 1.62:1 and the incidence of IVIGR was 17.9%. Multivariate regression analysis showed that the...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655234/ https://www.ncbi.nlm.nih.gov/pubmed/29113339 http://dx.doi.org/10.18632/oncotarget.21083 |
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author | Hua, Wang Sun, Yameng Wang, Ying Fu, Songling Wang, Wei Xie, Chunhong Zhang, Yiying Gong, Fangqi |
author_facet | Hua, Wang Sun, Yameng Wang, Ying Fu, Songling Wang, Wei Xie, Chunhong Zhang, Yiying Gong, Fangqi |
author_sort | Hua, Wang |
collection | PubMed |
description | OBJECTIVES: To clarify the independent risk factors and construct predictive model for intravenous immunoglobin (IVIG)-resistant KD (IVIGRKD). RESULTS: The ratio of male to female in the overall samples was 1.62:1 and the incidence of IVIGR was 17.9%. Multivariate regression analysis showed that the OR (95% CI) values of fever duration ≥ 7 days, delayed diagnosis, gamma-glutamyl transferase ≥ 25 U/L, serum sodium ≤ 135 mmol/L, neutrophil-to-lymphocyte ratio ≥ 2.8 and platelets ≤ 350 × 10(9)/L were 2.94 (2.17–4.00), 1.64 (1.07–2.53), 1.38 (1.07–1.79), 1.68 (1.30–2.19), 1.58 (1.22–2.06) and 1.39 (1.08–1.80), respectively. Based on these OR values, a new predictive model was established with an AUC of 0.685, a sensitivity of 60.7% and a specificity of 66.5%, and showed superiority to formerly reported models. Further analysis of patients ≤ 6 months old gave rise to improved predictions for IVIGRKD with an AUC of 0.746 relative the new model for the total samples. MATERIALS AND METHODS: A total of 2,126 KD cases were enrolled in this study. Clinical indicators showing significant differences were screened using univariate analysis, and the independent risk factors were further elucidated using multivariate regression analysis. A new model was constructed, and the predictive ability was evaluated with the area under the curve (AUC) value and the sensitivity and specificity by using the receiver operating characteristic (ROC) curve. CONCLUSIONS: The new model for predicting IVIGRKD in this study is superior to those reported previously, and further analysis of patients with IVIGRKD younger than 6 months old allowed optimization of the predictive model. |
format | Online Article Text |
id | pubmed-5655234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-56552342017-11-06 A new model to predict intravenous immunoglobin-resistant Kawasaki disease Hua, Wang Sun, Yameng Wang, Ying Fu, Songling Wang, Wei Xie, Chunhong Zhang, Yiying Gong, Fangqi Oncotarget Research Paper OBJECTIVES: To clarify the independent risk factors and construct predictive model for intravenous immunoglobin (IVIG)-resistant KD (IVIGRKD). RESULTS: The ratio of male to female in the overall samples was 1.62:1 and the incidence of IVIGR was 17.9%. Multivariate regression analysis showed that the OR (95% CI) values of fever duration ≥ 7 days, delayed diagnosis, gamma-glutamyl transferase ≥ 25 U/L, serum sodium ≤ 135 mmol/L, neutrophil-to-lymphocyte ratio ≥ 2.8 and platelets ≤ 350 × 10(9)/L were 2.94 (2.17–4.00), 1.64 (1.07–2.53), 1.38 (1.07–1.79), 1.68 (1.30–2.19), 1.58 (1.22–2.06) and 1.39 (1.08–1.80), respectively. Based on these OR values, a new predictive model was established with an AUC of 0.685, a sensitivity of 60.7% and a specificity of 66.5%, and showed superiority to formerly reported models. Further analysis of patients ≤ 6 months old gave rise to improved predictions for IVIGRKD with an AUC of 0.746 relative the new model for the total samples. MATERIALS AND METHODS: A total of 2,126 KD cases were enrolled in this study. Clinical indicators showing significant differences were screened using univariate analysis, and the independent risk factors were further elucidated using multivariate regression analysis. A new model was constructed, and the predictive ability was evaluated with the area under the curve (AUC) value and the sensitivity and specificity by using the receiver operating characteristic (ROC) curve. CONCLUSIONS: The new model for predicting IVIGRKD in this study is superior to those reported previously, and further analysis of patients with IVIGRKD younger than 6 months old allowed optimization of the predictive model. Impact Journals LLC 2017-09-19 /pmc/articles/PMC5655234/ /pubmed/29113339 http://dx.doi.org/10.18632/oncotarget.21083 Text en Copyright: © 2017 Hua et al. http://creativecommons.org/licenses/by/3.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Research Paper Hua, Wang Sun, Yameng Wang, Ying Fu, Songling Wang, Wei Xie, Chunhong Zhang, Yiying Gong, Fangqi A new model to predict intravenous immunoglobin-resistant Kawasaki disease |
title | A new model to predict intravenous immunoglobin-resistant Kawasaki disease |
title_full | A new model to predict intravenous immunoglobin-resistant Kawasaki disease |
title_fullStr | A new model to predict intravenous immunoglobin-resistant Kawasaki disease |
title_full_unstemmed | A new model to predict intravenous immunoglobin-resistant Kawasaki disease |
title_short | A new model to predict intravenous immunoglobin-resistant Kawasaki disease |
title_sort | new model to predict intravenous immunoglobin-resistant kawasaki disease |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655234/ https://www.ncbi.nlm.nih.gov/pubmed/29113339 http://dx.doi.org/10.18632/oncotarget.21083 |
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